Genetically diverse mice are novel and valuable models of age-associated susceptibility to Mycobacterium tuberculosis.

BACKGROUND: Tuberculosis, the disease due to Mycobacterium tuberculosis, is an important cause of morbidity and mortality in the elderly. Use of mouse models may accelerate insight into the disease and tests of therapies since mice age thirty times faster than humans. However, the majority of TB research relies on inbred mouse strains, and these results might not extrapolate well to the genetically diverse human population. We report here the first tests of M. tuberculosis infection in genetically heterogeneous aging mice, testing if old mice benefit from rapamycin. FINDINGS: We find that genetically diverse aging mice are much more susceptible than young mice to M. tuberculosis, as are aging human beings. We also find that rapamycin boosts immune responses during primary infection but fails to increase survival. CONCLUSIONS: Genetically diverse mouse models provide a valuable resource to study how age influences responses and susceptibility to pathogens and to test interventions. Additionally, surrogate markers such as immune measures may not predict whether interventions improve survival. Immun Ageing 2014 Dec 16; 11(1):24

Using Heterogeneous Stocks for Fine-Mapping Genetically Complex Traits.

In this chapter we will review both the rationale and experimental design for using Heterogeneous Stock (HS) populations for fine-mapping of complex traits in mice and rats. We define an HS as an outbred population derived from an intercross between two or more inbred strains. HS have been used to perform genome-wide association studies (GWAS) for multiple behavioral, physiological, and gene expression traits. GWAS using HS require four key steps, which we review: selection of an appropriate HS population, phenotyping, genotyping, and statistical analysis. We provide advice on the selection of an HS, comment on key issues related to phenotyping, discuss genotyping methods relevant to these populations, and describe statistical genetic analyses that are applicable to genetic analyses that use HS